Results 51 to 60 of about 277,892 (265)

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Safe Reinforcement Learning via Episodic Control

IEEE Access
Safe reinforcement learning (Safe RL) aims to learn policies capable of learning and adapting within complex environments while ensuring actions remain free from catastrophic consequences.
Zhuo Li, Derui Zhu, Jens Grossklags
doaj   +1 more source

Safe Reinforcement Learning in Uncertain Contexts

IEEE Transactions on Robotics
When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables, i.e., regression tasks. However, in practice, robotic systems are also subject to discrete, external environmental changes, e.g., having to carry objects of certain weights ...
Dominik Baumann, Thomas B. Schön
openaire   +4 more sources

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Spacecraft Safe Proximity Policy Based on Graph Neural Network Safe Reinforcement Learning

Aerospace
Spacecraft safe proximity, as a critical component of on-orbit servicing missions, primarily encounters the following two challenges: the partial observability of the environment surrounding the service spacecraft and the necessity to evade uncertain ...
Heng Zhou, Jingxian Wang, Monan Dong, Yong Zhao, Yuzhu Bai, Rong Chen   +5 more
doaj   +1 more source

A Survey of Safe Reinforcement Learning Methods in Robotics [PDF]

ITM Web of Conferences
Reinforcement learning has achieved remarkable results in complex decision-making tasks such as robot control, but its deployment in real-world environments still faces safety challenges.
Xie Yuen
doaj   +1 more source

Routing Using Safe Reinforcement Learning. [PDF]

, 2020
The ever increasing number of connected devices has lead to a metoric rise in the amount data to be processed. This has caused computation to be moved to the edge of the cloud increasing the importance of efficiency in the whole of cloud. The use of this fog computing for time-critical control applications is on the rise and requires robust guarantees ...
Gautham Nayak Seetanadi, Karl-Erik Årzén   +1 more
openaire   +3 more sources

PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science

Advanced Engineering Materials, EarlyView.
Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley   +1 more source

Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear Markov Decision Processes

IEEE Open Journal of Control Systems
We study data-driven computation of probabilistic controlled invariant sets (PCIS) for safety-critical reinforcement learning under unknown dynamics. Assuming a linear MDP model, we use regularized least squares and self-normalized confidence bounds to ...
Kazumune Hashimoto, Shunki Kimura, Junya Ikemoto, Kazunobu Serizawa, Yulong Gao, Kai Cai   +5 more
doaj   +1 more source

Adaptive Foam 3D Printing of Ultralight and Multifunctional Materials

Advanced Engineering Materials, EarlyView.
Adaptive foam 3D printing, enabled by expandable microspheres, imparts cellular structures to thermoplastic and thermosetting polymers, manufactured through a variety of processes including fused filament fabrication, direct ink writing, digital light processing, and inkjet printing.
Nariman Rajabifar, Amir Ameli
wiley   +1 more source